For each frame extracted from the raw videos, we adopt the CenterNet [1]} fine-tuned on our dataset as the person detector since it is an efficient anchor-free object detector 4,000 person bounding boxes are labeled for fine-tuning the detector..
To achieve accurate person tracking in videos, we exploit the Intersection-over-Union (IoU) and person re-identification (ReID) features of bounding boxes in two adjacent frames to measure their similarity.
The ReID feature is extracted by an open-source person ReID framework, FastReID https://github.com/JDAI-CV/fast-reid under the Apache 2.0 license. [2]} pretrained on several public person ReID datasets.
When two persons are highly overlapped, the tracking algorithm can easily misjudge them as one person, i.e., ID switching.
To solve this problem, we employ human annotators to clean sequences that may contain more than one pedestrian.
By this means, we guarantee that each sequence only belongs to one person.
Then, we discard the sequences shorter than 25 frames or longer than 500 frames and obtain about 50,000 sequences in total.
